Multi-core Research Articles

Additional damped rocking sections for enhancing seismic performance of self-centering dual rocking core systems

This paper aims to propose a novel self-centering dual rocking core system with damped multiple rocking sections (denoted as MSDRC system) for achieving enhanced seismic performance in mitigating high mode effects and responses. The configuration and desired seismic performance of the MSDRC system are introduced. The multiple rocking sections are introduced to reduce the structural member force demands caused by higher mode effects and buckling restrained braces (BRBs) are equipped in multiple rocking sections for absorbing higher mode energy. Comparative analyses set out to study the dynamic responses and highlight the benefits of the proposed MSDRC system compared to the existing self-centering dual rocking core (SDRC) and self-centering multiple rocking core (SMRC) systems. To facilitate an equitable comparison, the 9-story MSDRC, SDRC, and SMRC systems are engineered to attain identical target drift under design basis earthquakes (DBE). The analyses reveal that, in contrast to the SDRC system, the multiple rocking sections in SMRC and MSDRC systems can efficiently reduce the structural member force demands. Compared to the SMRC system, the BRBs in the multiple rocking sections in the MSDRC system can absorb sufficient energy to avoid inter-story drift concentration caused by higher mode responses under strong earthquakes. Parametric dynamic analyses are also performed to explore the influence of the capacity of BRBs on the dynamic nonlinear behaviors of the developed MSDRC system. The analysis results provide design recommendations for the design of BRBs in the rocking sections.

A self-adaptive parallel image stitching algorithm for unmanned aerial vehicles in edge computing environments

ABSTRACT The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2–10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV.

DESIGN AND IMPLEMENTATION OF REAL-TIME NON-LINEAR DYNAMIC SIMULATION OF FIGHTER AIRCRAFT ON MULTICORE PROCESSOR

Simulation is a system to imitate the operation of various kinds of real-world facilities or processes for training, behavior study or entertainment purposes. Some implementation of fighter aircraft simulation is done sequentially using FORTRAN and MATLAB / Simulink. On the other hand, a multicore (parallel) computer system has been in the personal computer environment, so the multicore computing paradigm should be considered. Intel Threading Building Blocks (TBB) library need to be utilized in multicore programming. This paper discusses the design and implementation of real-time nonlinear dynamic simulation of fighter aircraft on multicore processor. The aircraft model used in this study is F16 data. Implementation of the simulation code is written in C++ with Intel TBB, OpenGL, Open Scene Graph, and Open Dynamic Engine library. The testing results of non-linear dynamics simulation of fighter aircraft show that the speedup on an Intel Core 2 Duo processor is 1.1776 x for elevator doublet input (short period), while speedup on an Intel i7 is 1.5405 x for rudder doublet input (dutch roll). Key Words: multicore computing, real-time simulation, nonlinear dynamic, fighter aircraft model

Development of Smart Servers for Informatics Education Program Using NDLC Method

The development of processor technology, especially Multicore Processors, has had a significant impact in various fields, including the field of higher education, one of which is the need for Virtual Private Servers for information management and their potential as a storage space for web development products. This research aims to create a Virtual Private Server in the Informatics Education program at the Faculty of Mathematics and Natural Sciences of Hamzanwadi University. It uses the Research & Development (R&D) method with the Network Development Life Cycle (NDLC) approach, involving six stages: Analysis, Design, Prototype Simulation, Implementation, Monitoring, and Management. Data were obtained through interviews and questionnaires, then analyzed descriptively qualitatively. The research resulted in a Virtual Private Server using Proxmox VE 7.4, Ubuntu 18.04.5 operating system, remote access via OpenSSH-Server, Control Panel AAPanel 6.8.30, Nginx 1.21.4 web server, MySQL 5.7.34 database management system, PHP 7.4, PhpMyAdmin 5.0 for web access to the database, and PureFTPd 1.1.49 as the FTP Server. Expert evaluations in the field of IT yielded a rating of 83% with the category "Highly Suitable," indicating that the Virtual Private Server product is suitable for use as a storage facility for web-based development products in the Informatics Education program at the Faculty of Mathematics and Natural Sciences, Hamzanwadi University

DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor

With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these applications must process data continuously, in real-time, and extract relevant information using the smallest possible energy budgets. A promising approach for implementing always-on processing of sensory signals that supports on-demand, sparse, and edge-computing is to take inspiration from biological nervous system. Following this approach, we present a brain-inspired platform for prototyping real-time event-based spiking neural networks. The system proposed supports the direct emulation of dynamic and realistic neural processing phenomena such as short-term plasticity, NMDA gating, AMPA diffusion, homeostasis, spike frequency adaptation, conductance-based dendritic compartments and spike transmission delays. The analog circuits that implement such primitives are paired with a low latency asynchronous digital circuits for routing and mapping events. This asynchronous infrastructure enables the definition of different network architectures, and provides direct event-based interfaces to convert and encode data from event-based and continuous-signal sensors. Here we describe the overall system architecture, we characterize the mixed signal analog-digital circuits that emulate neural dynamics, demonstrate their features with experimental measurements, and present a low- and high-level software ecosystem that can be used for configuring the system. The flexibility to emulate different biologically plausible neural networks, and the chip’s ability to monitor both population and single neuron signals in real-time, allow to develop and validate complex models of neural processing for both basic research and edge-computing applications.

КОНЦЕПЦІЯ КООПЕРАТИВУ-ЛОКАЛЬНОЇ МІКРОМЕРЕЖІ ВДЕ ЯК ІНСТРУМЕНТ ІНКЛЮЗИВНОГО РОЗВИТКУ І ПІДТРИМКИ ЕНЕРГЕТИЧНОЇ БЕЗПЕКИ УКРАЇНИ

The article analyzes the prerequisites and presents the motivational grounds and concepts of creating digital platform cooperatives (DPs) as the basis for the formation of local microgrids of renewable energy generation (LMREG). The cooperation of small local RES producers, "Platform Cooperativeism", is considered as a tool of inclusive development, an alternative to "Platform Capitalism" as a result of the monopolization of markets by platforms - digital intermediaries.An overview of research on the development of cooperation of small REG producers is presented. The reasons hindering its development in Ukraine have been revealed. Attention is focused on the potential advantages that can be created by the use of the cross-subsidization effect inherent in the DP for the development of LMREG based on DP cooperatives. The structural and functional scheme of the cooperative DP of LMREG is proposed. Its structural components include the CPU core, the local self-government body, groups of legal entities and individuals - prosumers, who combine the roles of producers and consumers of electricity, as well as investors who invest in the development of the cooperative but do not have their power-generating equipment. The functional component provides generation, accumulation, and distribution of energy flows, their monitoring, data accumulation and analysis, algorithmic control and balancing of the state of the microgrid, provision of data for mutual calculations between its participants, and interaction with the unified energy system of Ukraine. The motivational reasons for participation in the Cooperative-DP of various groups of power generation equipment owners are analyzed. The possible consequences of the formation of the DP core - LMREG on a commercial basis are characterized.

3D Heat Conduction Solver for Complex Geometries

Abstract: The essential pre-requisites for any solver to tackle real life 3D problems are to handle complex geometries and quick turnaround time. In this direction an Unstructured Finite Volume (FVM) based multi-threaded heat conduction solver has been developed to address both the issues respectively. The solver is object oriented and written in Java language. Unstructured Finite Volume method has been utilized for the space discretization and a four stage Runge-Kutta scheme is used for time integration. This solution methodology is highly parallelizable and extensively used in CFD codes. The present solver can handle non-linear steady state and transient 3D thermal problems with Dirichlet, Neumann and surface radiation boundary conditions. The solver has been validated against bench mark problems. In the present work a 3D complex geometry is discretised into 2.5 lakh hexahedrons has been taken as a test case to demonstrate the efficiency of the solver with multi-threading in dual processor machines. The results obtained are presented.

Bayesian Phylogenetic Analysis on Multi-Core Compute Architectures: Implementation and Evaluation of BEAGLE in RevBayes With MPI.

Phylogenies are central to many research areas in biology and commonly estimated using likelihood-based methods. Unfortunately, any likelihood-based method, including Bayesian inference, can be restrictively slow for large datasets-with many taxa and/or many sites in the sequence alignment-or complex substitutions models. The primary limiting factor when using large datasets and/or complex models in probabilistic phylogenetic analyses is the likelihood calculation, which dominates the total computation time. To address this bottleneck, we incorporated the high-performance phylogenetic library BEAGLE into RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as well as hardware specific vectorized instructions for faster likelihood calculations. Our new implementation of RevBayes+BEAGLE retains the flexibility and dynamic nature that users expect from vanilla RevBayes. In addition, we implemented native parallelization within RevBayes without an external library using the message passing interface (MPI); RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using multi-threading on CPUs and 2 different powerful GPUs (NVidia Titan V and NVIDIA A100) against our native implementation of RevBayes+MPI. We found good improvements in speedup when multiple cores were used, with up to 20-fold speedup when using multiple CPU cores and over 90-fold speedup when using multiple GPU cores. The improvement depended on the data type used, DNA or amino acids, and the size of the alignment, but less on the size of the tree. We additionally investigated the cost of rescaling partial likelihoods to avoid numerical underflow and showed that unnecessarily frequent and inefficient rescaling can increase runtimes up to 4-fold. Finally, we presented and compared a new approach to store partial likelihoods on branches instead of nodes that can speed up computations up to 1.7 times but comes at twice the memory requirements.

Parallel Processing of Image Databases for Accelerated Morphological Analysis

Nuclear forensics relies on different signatures to identify the source of nuclear material. Such signatures include crystalline structure, chemical composition, and particle morphology. One way to quantify morphology in electron microscope imagery is through image segmentation, where pixels are assigned to several partitions (or groups) that correspond to particles, grains, and other objects of interest within the image. Once pixels are assigned to segments, it is relatively straightforward to quantify other quantities of interest, such as grain size, circularity, etc. However, the range and diversity of microscope images make it difficult to obtain an accurate segmentation automatically. The accuracy of segmentation can be improved through supervised learning, but this requires many images to be manually segmented. Another way to improve the accuracy is to use interactive segmentation. Interactive segmentation requires a human to provide image-specific user input to improve performance. However, the amount of user input (effort) is generally far less than is required for supervised learning. In this paper, we investigate several parallelization strategies to automatically explore the user input parameter space of interactive segmentation algorithms across a large number of images. Specifically, we investigate four different parallelization mechanisms in a high-performance computing (HPC) environment and use the Amdahl fraction to evaluate efficiency on multiple processor cores across multiple nodes. Ultimately, the parallelization strategy that was most efficient utilized the message passing interface integrated with the segmentation and quantification code. This strategy had an Amdahl fraction of 0.985 and a performance of about 0.251 s/image. These results indicate that the parameter space of interactive segmentation algorithms can be efficiently explored using HPC. This opens the door to future work where user input is reduced and where interactive image segmentation algorithms are automatically applied to large image sets.

Time-predictable task-to-thread mapping in multi-core processors

The performance of time-predictable systems can be improved in multi-core processors using parallel programming models (e.g., OpenMP). However, schedulability analysis of parallel applications is a big challenge due to their sophisticated structure. The common drawbacks of current task-to-thread mapping approaches in OpenMP are that they (i) utilize a global queue in the mapping process, which may increase contention, (ii) do not apply heuristic techniques, which may reduce the predictability and performance of the system, and (iii) use basic analytical techniques, which may cause notable pessimism in the temporal conditions. Accordingly, this paper proposes a task-to-thread mapping method in multi-core processors based on the OpenMP framework. The mapping process is carried out through two phases: allocation and dispatching. Each thread has an allocation queue in order to minimize contention, and the allocation and dispatching processes are performed using several heuristic algorithms to enhance predictability. In the allocation phase, each task-part from the OpenMP DAG is allocated to one of the allocation queues, which includes both sibling and child task-parts. A suitable thread (i.e., allocation queue) is selected using one of the suggested heuristic allocation algorithms. In the dispatching phase, when a thread is idle, a task-part is selected from its allocation queue using one of the suggested heuristic dispatching algorithms and then dispatched to and executed by the thread. The performance of the proposed method is evaluated under different conditions (e.g., varying the number of tasks and the number of threads) in terms of application response time and overhead of the mapping process. The simulation results show that the proposed method surpasses the other methods, especially in the scenario that includes overhead of the mapping. In addition, a prototype implementation of the main heuristics is evaluated using two kernels from real-world applications, showing that the methods work better than LLVM's default scheduler in most of the configurations.

Rapid measurement of epidermal thickness in OCT images of skin

Epidermal thickness (ET) changes are associated with several skin diseases. To measure ET, segmentation of optical coherence tomography (OCT) images is essential; manual segmentation is very time-consuming and requires training and some understanding of how to interpret OCT images. Fast results are important in order to analyze ET over different regions of skin in rapid succession to complete a clinical examination and enable the physician to discuss results with the patient in real time. The well-known CNN-graph search (CNN-GS) methodology delivers highly accurate results, but at a high computational cost. Our objective was to build a computational core, based on CNN-GS, able to accurately segment OCT skin images in real time. We accomplished this by fine-tuning the hyperparameters, testing a range of speed-up algorithms including pruning and quantization, designing a novel pixel-skipping process, and implementing the final product with efficient use of core and threads on a multicore central processing unit (CPU). We name this product CNN-GS-skin. The method identifies two defined boundaries on OCT skin images in order to measure ET. We applied CNN-GS-skin to OCT skin images, taken from various body sites of 63 healthy individuals. Compared with CNN-GS, our described method reduced computation time by 130 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} with minimal reduction in ET determination accuracy (from 96.38 to 94.67%).

Identifying similar-bicliques in bipartite graphs

Bipartite graphs have been widely used to model the relationship between entities of different types, where vertices are partitioned into two disjoint sets/sides. Finding dense subgraphs in a bipartite graph is of great significance and encompasses many applications. However, none of the existing dense bipartite subgraph models consider similarity between vertices from the same side, and as a result, the identified results may include vertices that are not similar to each other. In this work, we formulate the notion of similar-biclique which is a special kind of biclique where all vertices from a designated side are similar to each other and aim to enumerate all similar-bicliques. The naive approach of first enumerating all maximal bicliques and then extracting all maximal similar-bicliques from them is inefficient, as enumerating maximal bicliques is already time consuming. We propose a backtracking algorithm MSBE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{MSBE}$$\\end{document} to directly enumerate maximal similar-bicliques and power it by vertex reduction and optimization techniques. In addition, we design a novel index structure to speed up a time-critical operation of MSBE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf{MSBE}$$\\end{document}, as well as to speed up vertex reduction. Efficient index construction algorithms are developed. To handle dynamic graph updates, we also propose algorithms and optimization techniques for maintaining our index. Finally, we parallelize our index construction algorithms to exploit multiple CPU cores. Extensive experiments on 17 bipartite graphs as well as case studies are conducted to demonstrate the effectiveness and efficiency of our model and algorithms.

Population based metaheuristics in Spark: Towards a general framework using PSO as a case study

Over the years metaheuristics have been successfully applied to optimization problems in many real-world applications. The increasing complexity and scale of the problems addressed has posed new challenges to researchers in the field. The application of distributed metaheuristics is a common approach to speed up the time to solution or improve its quality by leveraging traditional parallel programming models on platforms like multicore processors or computer clusters. More recently, the emergence of Cloud Computing and new programming models and frameworks for Big Data has facilitated access to an unprecedented amount of computational resources, which led to a growing interest in optimization frameworks that support the development and execution of distributed metaheuristics taking advantage of this potential. In this paper, we present the current status of development of one such framework that aims to provide support for the application of distributed population-based metaheuristics to the global optimization of large-scale problems in Spark. The framework provides a reduced set of abstractions to represent the general structure of population-based metaheuristics as templates and strategies to particularize them into concrete metaheuristics, as well as other nice features like out of the box implementations of the most common distributed models, full configurability through a human-friendly format, and the possibility of rapid prototyping and testing metaheuristics in the Spark shell. To validate the approach, a template for Particle Swarm Optimization (PSO) was implemented as a proof of concept, which includes strategies for instantiating different variants of the algorithm, configurable topologies, and sequential and distributed execution models.

A deep learning technique to control the non-linear dynamics of a gravitational-wave interferometer

Abstract In this work we developed a deep learning technique that successfully solves a non-linear dynamic control problem. Instead of directly tackling the control problem, we combined methods in probabilistic neural networks and a Kalman-filter-inspired model to build a non-linear state estimator for the system. We then used the estimated states to implement a trivial controller for the now fully observable system. We applied this technique to a crucial non-linear control problem that arises in the operation of the Laser Interferometer Gravitational-Wave Observatory (LIGO) system, an interferometric gravitational-wave observatory. We demonstrated in simulation that our approach can learn from data to estimate the state of the system, allowing a successful control of the interferometer’s mirror. We also developed a computationally efficient model that can run in real time at high sampling rate on a single modern CPU core, one of the key requirements for the implementation of our solution in the LIGO digital control system. We believe these techniques could be used to help tackle similar non-linear control problems in other applications.

Heat transfer analysis in particle-laden flows using the immersed boundary method

This paper investigates an efficient immersed boundary method (IBM) for multiple-core CPU machines with local grid refinement for the calculation of heat transfer between fluids and finite-sized particles. The fluid momentum equations are solved by using the fractional step method, while the energy equation is solved by employing the second-order Adams-Bashforth method. For efficient load balancing between the CPU cores, the coupling between particles and fluid is obtained by applying the body force in the fluid equations, which depends on the solid volume fraction of particles contained in each grid cell, and then by linearly interpolating the particle temperature and velocity on the fluid grid cell (in place of the delta function commonly used in literature). Several test cases from the literature are studied, and good agreement is observed between the simulation results and the literature. Finally, a scaling study on multiple core machines is performed, demonstrating the proposed IBM's capabilities for a significant reduction in processing time.

Many-core algorithms for high-dimensional gradients on phylogenetic trees.

Advancements in high-throughput genomic sequencing are delivering genomic pathogen data at an unprecedented rate, positioning statistical phylogenetics as a critical tool to monitor infectious diseases globally. This rapid growth spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences N. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes O(N2) operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in O(N), enabling researchers to take advantage of gradient-based samplers such as HMC. The CPU implementation of this approach makes the calculation of the gradient computationally tractable for nucleotide-based models but falls short in performance for larger state-space size models, such as Markov-modulated and codon models. Here, we describe novel massively parallel algorithms to calculate the gradient of the log-likelihood wrt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold higher speedups over previous CPU implementations. We benchmark these GPU algorithms on three computing systems using three evolutionary inference examples exploring complete genomes from 997 dengue viruses, 62 carnivore mitochondria and 49 yeasts, and observe a >128-fold speedup over the CPU implementation for codon-based models and >8-fold speedup for nucleotide-based models. As a practical demonstration, we also estimate the timing of the first introduction of West Nile virus into the continental Unites States under a codon model with a relaxed molecular clock from 104 full viral genomes, an inference task previously intractable. We provide an implementation of our GPU algorithms in BEAGLE v4.0.0 (https://github.com/beagle-dev/beagle-lib), an open-source library for statistical phylogenetics that enables parallel calculations on multi-core CPUs and GPUs. We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (https://github.com/beast-dev/beast-mcmc).

Forward Learning of Large Language Models by Consumer Devices

Large Language Models achieve state of art performances on a broad variety of Natural Language Processing tasks. In the pervasive IoT era, their deployment on edge devices is more compelling than ever. However, their gigantic model footprint has hindered on-device learning applications which enable AI models to continuously learn and adapt to changes over time. Back-propagation, in use by the majority of deep learning frameworks, is computationally intensive and requires storing intermediate activations into memory to cope with the model’s weights update. Recently, “Forward-only algorithms” have been proposed since they are biologically plausible alternatives. By applying more “forward” passes, this class of algorithms can achieve memory reductions with respect to more naive forward-only approaches and by removing the need to store intermediate activations. This comes at the expense of increased computational complexity. This paper considered three Large Language Model: DistilBERT, GPT-3 Small and AlexaTM. It investigated quantitatively any improvements about memory usage and computational complexity brought by known approaches named PEPITA and MEMPEPITA with respect to backpropagation. For low number of tokens in context, and depending on the model, PEPITA increases marginally or reduces substantially arithmetic operations. On the other hand, for large number of tokens in context, PEPITA reduces computational complexity by 30% to 50%. MEMPEPITA increases PEPITA’s complexity by one third. About memory, PEPITA and backpropagation, require a comparable amount of memory to store activations, while MEMPEPITA reduces it by 50% to 94% with the benefits being more evident for architectures with a long sequence of blocks. In various real case scenarios, MEMPEPITA’s memory reduction was essential for meeting the tight memory requirements of 128 MB equipped edge consumer devices, which are commonly available as smartphone and industrial application multi processors.

Role of structural asymmetry on the delivery of all-optical logical binary half adder in an asymmetric triple core photonic crystal fiber

This article explores the influence of structural asymmetry on the delivery of all-optical logical binary half adder function through triple-core photonic crystal fiber. For the proposed aim, the study considers different configurations of symmetric as well as asymmetric triple-core photonic crystal fiber involving optical guiding cores arranged in a planar and triangular fashion. The role of geometrical asymmetry is investigated by considering an asymmetric triple core photonic crystal fiber with a minor variation in one of the guiding core radii. Through the extinction ratio calculations, with suitable input signal combinations and control signal power, the half adder operation is demonstrated via logic outputs. The obtained results show the significant role of geometrical asymmetry indicated by the variation in the extinction ratios for the individual configurations. However, the half adder operation is found to be robust even in the presence of considerable geometrical asymmetry, with a significant figure of merit. Apart from this, a minor shift in the control signal phase is also observed between symmetric and asymmetric configurations for delivering logic gates based on the half adder function.

Near-Infrared Off-Axis integrated cavity output spectroscopic sensor system for ammonia leakage measurement in cold store using embedded Multi-Core processor

A near-infrared off-axis integrated cavity output spectroscopic (OA-ICOS) sensor system for ammonia (NH3) leakage detection was developed. A distributed feedback (DFB) laser was used to target the absorption line of NH3 at 6612.167 cm−1. Experiments were conducted to evaluate the performance of the sensor system. When the averaging time is 0.8 s, the detection limit (1σ) of NH3 is ∼ 1.21 parts-per-million by volume (ppmv) using wavelength modulation spectroscopy (WMS). The 10–90 % response time of the NH3 sensor system is ∼ 7.2 s. Based on the developed local monitoring and control platform (LMCP), cloud server, remote monitoring and control end (RMCE), the unattended NH3 leakage detection experiments were carried out in a cold store located in Jilin City. The experimental results confirmed the rapid detection capability of the reported sensor system for NH3 leakage as well as the capability of remote monitoring and control.

Analysing the differential impact of the COVID-19 pandemic on the resilience of the tourism economy: A case study of the Chengdu-Chongqing urban agglomeration in China

The outbreak of the COVID-19 pandemic in 2019 had a profound impact on the tourism economy, underscoring the critical importance of assessing and analyzing tourism economic resilience. Traditionally, prior research predominantly focused on constructing evaluation systems based on three dimensions: Risk preparedness capability, Restoration capability, and Reorganization and modernization capacity. In this study, we take an innovative approach by incorporating urban network thinking and establishing a tourism economic network, while introducing the dimension of “The rationality of network structure.” To comprehensively understand the dynamics of tourism economic resilience, we divided the period from 2018 to 2021 into three distinct phases: Stable period, Pre-shock period, and Shock period. This division allowed us to conduct comparative research that highlights the variations in tourism economic resilience across these different time frames. Additionally, we employed advanced methods, such as kernel density estimation and the GTWR model, for empirical analysis of tourism economic resilience within the Chengdu-Chongqing city cluster. The results of our research unveiled that Chengdu and Chongqing both demonstrate a remarkable level of resilience within their tourism economies. However, given their status as the “dual core” of the Chengdu-Chongqing city agglomeration, they are inherently more susceptible to significant fluctuations when confronted with shocks. The spatial pattern of tourism economic resilience is characterized by prominent wings on both sides, a North-South balance, and a central region with vulnerabilities. The predominant evolutionary patterns are marked by multi-level stabilization, moderate growth, and moderate decline. Ziyang is an exceptional region within the broader growth zone, and its reduced risk preparedness capability has led to an overall decline in tourism economic resilience. Furthermore, key influencing factors, including the economy, infrastructure, and ecological environment, exhibit significant spatial heterogeneity. In conclusion, our study offers valuable insights into researching tourism economic resilience under external shocks, such as the COVID-19 pandemic. These findings can be instrumental in guiding policymakers as they develop effective strategies to bolster tourism economic resilience.